Volume 17, Issue 3 - Special Issue: Second International Symposium in Computational Economics and Finance Issue Editors: Gilles Dufrénot and Fredj Jawadi

Although the ERM II rules allow the Danish krone to fluctuate against the euro within an official target zone of 4.5%, most of the time the exchange rate has remained in a narrow range around its unconditional mean. Estimating a Smooth Transition Autoregression Target Zone (STARTZ) model confirms that the exchange rate exhibits target zone dynamics consistent with a band of approximately 0.75% around its unconditional mean. We conclude that the Danmark Nationalbank intervention policy of intra-marginal operations successfully managed an informal target zone in the foreign exchange market.

We propose a multivariate nonlinear econometric time series model, which can be used to examine if there is common nonlinearity across economic variables. The model is a multivariate censored latent effects autoregression. The key feature of this model is that nonlinearity appears as separate innovation-like variables. Common nonlinearity can then be easily defined as the presence of common innovations. We discuss representation, inference, estimation and diagnostics. We illustrate the model for US and Canadian unemployment and find that US innovation variables have an effect on Canadian unemployment, and not the other way around, and also that there is no common nonlinearity across the unemployment variables.

Empirical studies often report a negative relationship between the difference in the spot exchange rate and the forward premium, violating the forward-rate unbiasedness hypothesis. Using standard regression on a sample of ten exchange rates, we obtain both positive and negative coefficients. We argue that the negative coefficients could arise as a result of the non-linearities in the relationship and misspecification. As an alternative to the standard regression, we use a time-varying-coefficient technique that estimates bias-free coefficients and, thus, should provide better estimates of the link between spot and forward rates. Our findings strongly support the forward rate unbiasedness hypothesis. All the parameters are very close to unity and significant.

This paper proposes a new fractional model with a time-varying long-memory parameter. The latter evolves nonlinearly according to a transition variable through a logistic function. We present an LR-Based test that allows to discriminate between the standard fractional model and our model. We further apply a nonlinear least squares estimation method to estimate the long-memory parameter. We present an application to the unemployment rate in the United States from 1948 to 2012.

This paper assesses the performance of linear and nonlinear causality tests in the presence of multivariate conditional heteroskedasticity, exogenous volatility regressors, and additive volatility outliers. Monte Carlo simulations show that tests based on the least squares covariance matrix estimator can frequently lead to finding spurious Granger causality. The degree of oversizing tends to increase with the sample size and is substantially larger for the nonlinear test. On the other hand, heteroskedasticity-robust tests which are based on the fixed design wild bootstrap perform adequately in terms of size and power. Consequently, reliable causality in mean tests can be conducted without the need to specify a conditional variance function. As an empirical application, we re-examine the return-volume relationship.

This paper studies volatility dynamics and provides further insights into its relationship with trading volume. In particular, we examine whether trading volume is significantly informative for investors when attempting to apprehend potential changes in volatility dynamics, and hence, in the evolution of market risk. To this end, we apply recent nonlinear modeling tools, namely Switching Transition Regression (STR) models that are robust to asymmetry and nonlinearity as well as TARCH models to check for the nature of transition between volatility regimes. Our findings show that volatility dynamics exhibit nonlinearity and switching regimes for which the transition is smooth rather than abrupt. Furthermore, one regime is associated with low volatility and a weak relationship with trading volume while in the second regime, the causality relationship is stronger and volatility is high. The paper’s novelty is to show that not only does trading volume contribute to explaining market volatility, but also that the change in volatility dynamics is performed through the change in its relationship with trading volume.

From quarterly postwar US and French data, this paper provides evidence of a bounce-back effect in inventory investment but not in final sales data. Actually, from a bounce-back augmented threshold model, it appears that i) the null hypothesis of no bounce-back effect is strongly rejected by the inventory investment data and ii) the one-step ahead forecasting performances of the models accounting for this bounce-back effect are well improved compared to linear or standard threshold autoregressions. This supports the conventional wisdom that inventory investment exacerbates aggregate fluctuations, in line with the recent theoretical models by, e.g., Wang and Wen (Wang, P., and Y. Wen. 2009. “Inventory Accelerator in General Equilibrium.” Working Paper 010, Federal Reserve Bank of St. Louis) and Wang, Wen and Xu (Wang, P., Y. Wen, and Z. Xu. 2011. “When do Inventories Destabilize the Economy? An Analytical Approach to (s,s) Policies.” Working Paper 014, Federal Reserve Bank of St. Louis) which clearly predict a destabilizing role of inventory investment over the business cycle. By contrast, our empirical findings cast doubt on models based on the stockouts avoidance motive for holding inventories.